The drive to make utilities more efficient through AI, machine learning, and data science has resulted in major benefits for every actor in the energy sector, including generators, distributors, the environment, taxpayers, and consumers. There is still much to do, however, and resource and utility companies that hope to remain competitive in the coming years should be aggressively pursuing the next technological frontier.
To be sure, there are plenty of high-value use cases in the utilities and energy industries (read all about them in the latest white paper). But success in AI isn’t just about choosing the right use case - it’s about organizational change.
Powering Change Around Data
Organizational change at any level is not easy, which is why so few enterprises - even in the midst of the current AI revolution - have managed to pivot to become completely data-driven companies. Cutting-edge companies in the utility and energy sector are able to make the switch through a combination of:
People, putting data in the hands of the many (including on-the-ground technicians, customer support, or business people) instead of siloing it for use only by certain people or teams.
Process, making it easy to access data, use it for business insights, and (when it makes sense) putting it in production for operationalized projects.
Technology, selecting the right tools to enable people and processes. This means planning for a responsible, scalable, and elastic AI strategy that will be sustainable for the long term.
Choosing the Right Use Case
On top of aligning people, process, and technology, utility and energy companies looking to up their data game face another challenge, which is: with dozens of potential use cases but limited resources, how can organizations prioritize the right projects?
An ideal AI project will have clear and compelling answers to each of these questions:
- WHO will this project benefit?
- HOW will it specifically improve experience or outcomes, and HOW can this be measured?
- WHY is using AI for this purpose better than existing processes?
- WHAT is the upside if it succeeds, and WHAT are the consequences if it fails?
- WHERE will the data come from, and does it already exist?
- WHEN should an initial working prototype and, subsequently, a final solution in production be delivered?